Detection of Chip Layered Defects Based on Dual Focus Mechanism

Abstract

Chip layering defects affect the performance of chips and lead to the failure of chips, so chip layering defects detection is an important step in the quality acceptance of chip production. Chip layering defects, which are characterized by insignificant color change in defect area, small defect area and difficult localization, bring challenges to traditional detection. In recent years, deep learning has shown its powerful ability to solve complex problems in computer vision. In this paper, semantic segmentation method is used to study the problem of chip hierarchical defect detection. Dual focus mechanism first applies whiteboard network structure to identify the true hierarchical area. Afterwards the defective layer area and the original map, the layered defect is recognized in the whiteboard attention. Since the contrast of the layered defect is not obvious, the precise layered defect tag extraction is another important factor affecting network performance. Based on the fuzzy-c-mean clustering algorithm and expert acceptance principle, obtaining the precise layered defect label, the practicality of this method is further enhanced. The effectiveness of the method for detecting the chip layering defects is verified by testing the chip image provided by Huawei.